The electrocardiogram (ECG) is a well-established and easy to obtain physiological signal of remarkable diagnostic power. It is composed of the concatenation of single ECG beats, which themselves can be split into single waves (P,Q,R,S,T wave). Most of its clinically useful information is given by the amplitudes and
durations of the single waves as well as the time intervals between them. Thus, automated ECG beat detection and the subsequent segmentation into the beats' waves has been an important subject of research during the past decades. Algorithms have to be capable of dealing with inter-individual morphology as well as common artifacts, characteristic for ECG recordings. Consequently, there exist many approaches for ECG beat segmentation. One of the most promising algorithms, however, is based on the wavelet transform. In this work we introduce an improvement of this approach, leading to an algorithm which is especially suited to detect minimal changes of the characteristic intervals and amplitudes in a patient's ECG over time.